910 publications from this institution
This article proposes several criteria for the distribution of roots of quasi-polynomials of neutral type with complex coefficients. Compared with Pontryagin's results, the derived criteria can be numerically implemented because the interval of the frequency for analyzing the behavior of the quasi-polynomial can be determined. Moreover, some Hurwitz stability criteria to judge whether all the roots of the quasi-polynomials are in the open left-half complex plane are provided. These Hurwitz stability criteria can be employed to analyze the stability of linear time-invariant systems with commensurate delays. It should be pointed out that on the one hand, the derived criteria are general since quasi-polynomials of retarded type and quasi-polynomials with real coefficients are their special cases. On the other hand, the conditions in Hurwitz stability criteria are all necessary and sufficient. Furthermore, as a special case, several criteria for the distribution of roots of the quasi-polynomials with real coefficients are presented. For the proposed criteria, this article provides some examples to illustrate the implementation and presents the detailed analysis and proofs.
Stealing attack against controlled information, along with the increasing number of information leakage incidents, has become an emerging cyber security threat in recent years. Due to the booming development and deployment of advanced analytics solutions, novel stealing attacks utilize machine learning (ML) algorithms to achieve high success rate and cause a lot of damage. Detecting and defending against such attacks is challenging and urgent so that governments, organizations, and individuals should attach great importance to the ML-based stealing attacks. This survey presents the recent advances in this new type of attack and corresponding countermeasures. The ML-based stealing attack is reviewed in perspectives of three categories of targeted controlled information, including controlled user activities, controlled ML model-related information, and controlled authentication information. Recent publications are summarized to generalize an overarching attack methodology and to derive the limitations and future directions of ML-based stealing attacks. Furthermore, countermeasures are proposed towards developing effective protections from three aspects -- detection, disruption, and isolation.
